Type 2 diabetes after a pregnancy with gestational diabetes among first nations women in Australia: The PANDORA study
Bibliographic record
Abstract
AIMS: To determine among First Nations and Europid pregnant women the cumulative incidence and predictors of postpartum type 2 diabetes and prediabetes and describe postpartum cardiovascular disease (CVD) risk profiles. METHODS: PANDORA is a prospective longitudinal cohort of women recruited in pregnancy. Ethnic-specific rates of postpartum type 2 diabetes and prediabetes were reported for women with diabetes in pregnancy (DIP), gestational diabetes (GDM) or normoglycaemia in pregnancy over a short follow-up of 2.5 years (n = 325). Pregnancy characteristics and CVD risk profiles according to glycaemic status, and factors associated with postpartum diabetes/prediabetes were examined in First Nations women. RESULTS: The cumulative incidence of postpartum type 2 diabetes among women with DIP or GDM were higher for First Nations women (48%, 13/27, women with DIP, 13%, 11/82, GDM), compared to Europid women (nil DIP or GDM p < 0.001). Characteristics associated with type 2 diabetes/prediabetes among First Nations women with GDM/DIP included, older age, multiparity, family history of diabetes, higher glucose values, insulin use and body mass index (BMI). CONCLUSIONS: First Nations women experience a high incidence of postpartum type 2 diabetes after GDM/DIP, highlighting the need for culturally responsive policies at an individual and systems level, to prevent diabetes and its complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".